Computer Vision with Python Training Course

This intensive, practical course explores advanced Python techniques, engineering best practices, and widely-used design patterns to help you build maintainable, testable, and high-performance Python applications. It focuses on modern tooling, type hints, concurrency models, architectural patterns, and deployment-ready workflows.

Delivered as instructor-led live training (online or onsite), this program is designed for intermediate to advanced Python developers who want to adopt professional practices and patterns for production-grade Python systems.

Upon completion of this training, participants will be able to:

• Apply Python typing, dataclasses, and type-checking to enhance code reliability.
• Utilise design patterns and architectural principles to structure robust applications.
• Implement concurrency and parallelism effectively using asyncio and multiprocessing.
• Write well-tested code using pytest, property-based testing, and CI pipelines.
• Profile, optimise, and harden Python applications for production environments.
• Package, distribute, and deploy Python projects using modern tools and containers.

Format of the Course

• Interactive lectures and short demonstrations.
• Hands-on labs and coding exercises each day.
• A capstone mini-project integrating patterns, testing, and deployment.

Course Customization Options

• To request a customized training session or focus on specific areas (data, web, or infrastructure), please contact us to arrange.

This programme imparts practical engineering methodologies for designing, building, testing, and deploying agentic (autonomous) systems using Python. It encompasses the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production considerations.

This instructor-led, live training (available online or onsite) is targeted at intermediate to advanced ML engineers, AI developers, and software engineers who wish to construct robust, production-ready autonomous agents using Python.

Upon completion of this training, participants will be capable of:

• Designing and implementing the agent loop and decision-making workflows.
• Integrating external tools and APIs to extend agent capabilities.
• Implementing short-term and long-term memory architectures for agents.
• Coordinating multi-step orchestrations and agent composability.
• Applying safety, access control, and observability best practices for deployed agents.

Course Format

• Interactive lecture and discussion.
• Hands-on labs building agents with Python and popular SDKs.
• Project-based exercises that produce deployable prototypes.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This programme offers a hands-on exploration of Data Science and Artificial Intelligence through Python, empowering professionals with the capabilities to analyse data, develop machine learning models, and implement AI-driven solutions within commercial settings. The curriculum encompasses CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning techniques, deep learning using TensorFlow, natural language processing, big data analytics via Spark, and data-driven storytelling. It is particularly suited for novices pursuing Python data science certification and practical analytics training to enhance career prospects.

Artificial Intelligence with Python focuses on creating intelligent systems by leveraging Python's comprehensive ecosystem of AI and machine learning libraries.

This instructor-led live training, available both online and onsite, is tailored for intermediate-level Python programmers who aim to design, implement, and deploy AI solutions using Python.

Upon completion of this training, participants will be able to:

• Implement AI algorithms using Python’s core AI libraries.
• Work with supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions into existing applications and workflows.
• Evaluate model performance and optimise for accuracy and efficiency.

Format of the Course

• Interactive lecture and discussion.
• Ample exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.

This instructor-led, live training (available online or onsite) is designed for intermediate-level AI practitioners who want to build, deploy, and optimize vision and language models for production environments using the CANN SDK.

By the conclusion of this training, participants will be able to:

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Utilize CANN tools to convert models and integrate them into live pipelines.
• Optimize inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines for both edge and cloud-based deployment scenarios.

Course Format

• Interactive lectures and demonstrations.
• Hands-on labs involving model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course Customization Options

• To request a customized training session for this course, please contact us to make arrangements.

This instructor-led, live training in Botswana (online or onsite) is aimed at intermediate-level AI developers and computer vision engineers who wish to build robust vision systems for autonomous driving applications.

By the end of this training, participants will be able to:

• Understand the fundamental concepts of computer vision in autonomous vehicles.
• Implement algorithms for object detection, lane detection, and semantic segmentation.
• Integrate vision systems with other autonomous vehicle subsystems.
• Apply deep learning techniques for advanced perception tasks.
• Evaluate the performance of computer vision models in real-world scenarios.

This instructor-led, live training in Botswana (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in Botswana (online or on-site) is designed for intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

Upon completion of this training, participants will be able to:

• Configure a development environment that includes Python, Pandas, and NumPy.
• Develop a data analysis application using Pandas and NumPy.
• Execute advanced data wrangling, sorting, and filtering operations.
• Perform aggregate operations and analyse time series data.
• Create data visualizations using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

This instructor-led, live training in Botswana (online or onsite) is designed for intermediate to advanced computer vision engineers, AI developers, and IoT professionals who wish to implement and optimise computer vision models for real-time processing on edge devices.

Upon completion of this training, participants will be able to:

• Grasp the fundamentals of Edge AI and its applications in computer vision.
• Deploy optimised deep learning models on edge devices for real-time image and video analysis.
• Utilise frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimise AI models for performance, power efficiency, and low-latency inference.

This instructor-led, live training in Botswana (online or in-person) is designed for beginner-level law enforcement officers who wish to move from manual facial sketching to using AI tools for developing facial recognition systems.

Upon completing this training, participants will be able to:

• Grasp the core principles of Artificial Intelligence and Machine Learning.
• Learn the fundamentals of digital image processing and its role in facial recognition.
• Acquire skills in utilizing AI tools and frameworks to construct facial recognition models.
• Gain practical experience in building, training, and evaluating facial recognition systems.
• Understand the ethical implications and best practices associated with facial recognition technology.

This instructor-led live training, offered online or on-site, is designed for developers who wish to leverage the FARM (FastAPI, React, and MongoDB) stack to create dynamic, high-performance, and scalable web applications.

By the end of this training, participants will be able to:

• Set up the necessary development environment that integrates FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led, live training in Botswana (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs easier and faster.

By the end of this training, participants will be able to:

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led live training in Botswana (online or onsite) is aimed at beginner to intermediate researchers and laboratory professionals who wish to process and analyse images related to histological tissues, blood cells, algae, and other biological samples.

By the end of this training, participants will be able to:

• Navigate the Fiji interface and utilize ImageJ’s core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyze images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows for specific image analysis needs in biological research.

This instructor-led, live training in Botswana (online or onsite) is aimed at intermediate-level professionals who wish to use Vision Builder AI to design, implement, and optimize automated inspection systems for SMT (Surface-Mount Technology) processes.

By the end of this training, participants will be able to:

• Set up and configure automated inspections using Vision Builder AI.
• Acquire and preprocess high-quality images for analysis.
• Implement logic-based decisions for defect detection and process validation.
• Generate inspection reports and optimize system performance.

This instructor-led live training in Botswana (online or onsite) targets intermediate to advanced developers, researchers, and data scientists keen on mastering the implementation of real-time object detection using YOLOv7.

Upon completion of this training, participants will be capable of:

• Grasping the core concepts of object detection.
• Installing and configuring YOLOv7 for object detection tasks.
• Training and evaluating custom object detection models with YOLOv7.
• Integrating YOLOv7 with other computer vision frameworks and tools.
• Resolving common issues associated with YOLOv7 implementation.